Quantitative Methods in Regional Science: Perspectives on Research Directions

1991 ◽  
pp. 403-424 ◽  
Author(s):  
L. Anselin
2020 ◽  
Vol 12 (19) ◽  
pp. 8191
Author(s):  
Khalifa Mohammed Al-Sobai ◽  
Shaligram Pokharel ◽  
Galal M. Abdella

Strategic projects are large scale, complex, and require significant investments and resources. These projects aim at gaining long-term social and economic benefits. Therefore, organizations focusing on strategic projects should use a consistent approach that suits their strategy, capability, and long-term expectations. Based on the four research questions and content analysis of the literature, generic processes used for the strategic project selection in tandem with the managerial capabilities are identified in this paper. The generic processes and managerial capabilities are used to develop a generic framework for strategic project selection. The framework is used for literature analysis in the paper. The review shows that both qualitative and quantitative methods are used for strategic project selection. Some possible research directions have also been proposed at the end of the review. The paper provides value to both researchers and practitioners in terms of tools available and a guidance on project selection through a structured process framework.


2021 ◽  
pp. 231971452110626
Author(s):  
Jishnu Bhattacharyya ◽  
Manoj Kumar Dash

The literature on telecommunications customer churn behaviour has grown in importance and volume since the early 2000s. This study performed a quantitative bibliometric retrospection of selected journals that qualified for the ABDC journal quality list to examine relevant studies published by them on customer churn research in telecommunication. Using bibliometric data from 175 research articles available in the Scopus database, this review sheds light on the publication trends, articles, stakeholders, prevalent research techniques, and topics of interest over three decades (1985–2019). According to the findings of this review, the current level of contributions are manifested through ten overarching groups of scholarship—namely churn prediction and modelling, feature selection techniques and comparison, customer retention strategy and relationship management, service recovery, pricing and switching cost, legislation, legal, and policy, word-of-mouth and post-switching behaviour, new service adoption, brand credibility, and loyalty. The existing literature has predominantly utilized quantitative methods to their full potential. For far too long, scholars, according tothe study’s central thesis, have ignored the metatheoretical consequences of relying solely on a logical positivism paradigm. In addition, we highlight research directions and the need for customer churn research to go beyond feature selection and modelling.


Author(s):  
David R. Legates ◽  
Sucharita Gopal

Although the use of mathematical models and quantitative methods in geography accelerated in earnest with the development of quantitative geography and regional science in the late 1950s, such techniques had already made their way into the mainstream of physical geography much earlier. Today, mathematical models and quantitative methods are used in a number of subfields in geography with their proliferation being aided, in part, by the widespread use of remote sensing, geographic information systems (GIS), and computer-based technology. As a consequence, geography as a whole has witnessed a new growth in the development of models and quantitative methods over the last decade, and it is this growth that we seek to elucidate here. Highlighting the advances in the use of models and methods in geography is a difficult undertaking. Such techniques are so widely used in GIS and remote sensing that many developments in these areas also could be considered in this chapter. Moreover, modeling and quantitative techniques are so strongly integrated within some geographic subfields (e.g. climatology and geomorphology, economic and urban geography, regional science) that it is often difficult to separate technique development from application. This is illustrated by the fact that many members of the Association of American Geographers who frequently use and develop quantitative techniques and models are not active participants in the Mathematical Models and Quantitative Methods Specialty Group, choosing instead to favor specialty groups with a more topical, rather than methodological, focus. In a very real sense, the quantitative revolution has been completed in many subfields of geography, with the goals and aims of the revolutionaries having long since passed into the mainstream. Furthermore, geographers who are involved with quantitative methods and mathematical models are extremely diverse in their interests and applications— they contribute to an extremely wide variety of disciplines. While they excel at spreading the geographic word to other disciplines, summarizing their multifarious contributions is nearly impossible. The rather trite statement, “Geography is what geographers do,” seems to apply strongly here. Geographers are largely a collection of individuals who, although united by their interest in spatial models and methods, are unique in the ways that they make contributions to various fields.


Author(s):  
Katarzyna Kopczewska

AbstractThis paper is a methodological guide to using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data, and supervised learning, which displaces classical spatial econometrics. It shows the potential of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are combined with spatial data integration, modelling, model fine-tuning and predictions to deal with spatial autocorrelation and big data. The paper delineates “already available” and “forthcoming” methods and gives inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.


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